The principal component analysis method combined with genetic algorithm and neural network was used to establish the sensory evaluation model based on the endogenous aromatic components of tobacco.GC-MS method was used to qualitatively and quantitatively analyze the endogenous aromatic components of tobacco essential oil obtained by supercritical extraction and molecular distillation.Initially,principal component analysis(PCA) was used to analyse endogenous aromatic components.Scores of the five extracted principal components and sensory evaluation were then used as the input and output variables,respectively.Back-propagation(BP) neural network was used to establish the prediction model.Genetic algorithm(GA) was further applied to optimize the neural network weights and thresholds.The experimental results showed that the performance of GA-BP model was better than that of BP.The correlation coefficient between the predicted value by the GA-BP model and the experimental value was 0.96,and the root mean square error of prediction(RMSEP) was 1.81.The GA-BP model showed better fitting ability and prediction ability.The model could effectively predict the sensory quality of the essential oil.